# Analysing survey responses: running the plan

The analysis in surveyframe is driven by the plan stored in the instrument. Once responses are imported, scored, and checked, `run_analysis_plan()` runs every research question in one pass and returns results formatted for reporting.

The worked-study vignette uses a published study whose data is private. This vignette uses the bundled tourism demo, a synthetic dataset shaped around the same digital marketing and tourism constructs, so the analysis runs end to end without internet access or private data.

## Load the demo

    demo      <- sframe_demo_data()
    instr     <- demo$instrument
    responses <- demo$responses

    dim(responses)
    #> [1] 120  18

## Import responses

Response data uses instrument item IDs as column names. Metadata columns are declared explicitly. Use `strict = TRUE` to keep only known columns.

    responses <- read_responses(
      demo$responses_path,
      instr,
      respondent_id = "respondent_id",
      submitted_at  = "submitted_at",
      meta_cols     = "started_at",
      strict        = TRUE
    )

    dim(responses)
    #> [1] 120  18

## Missing data and quality

    mr <- missing_data_report(responses, instr)
    kable(mr$item_missing, digits = 2,
          col.names = c("Variable", "Missing (n)", "Missing (%)", "Valid (n)"),
          caption = "Item-level missingness")

Item-level missingness
| Variable | Missing (n) | Missing (%) | Valid (n)
--- | --- | --- | --- | ---
visit_type | visit_type | 0 | 0 | 120
dm_1 | dm_1 | 0 | 0 | 120
dm_2 | dm_2 | 0 | 0 | 120
dm_3 | dm_3 | 0 | 0 | 120
sq_1 | sq_1 | 0 | 0 | 120
sq_2 | sq_2 | 0 | 0 | 120
sq_3 | sq_3 | 0 | 0 | 120
sus_1 | sus_1 | 0 | 0 | 120
sus_2 | sus_2 | 0 | 0 | 120
sat_1 | sat_1 | 0 | 0 | 120
sat_2 | sat_2 | 0 | 0 | 120
bi_1 | bi_1 | 0 | 0 | 120
bi_2 | bi_2 | 0 | 0 | 120
attention | attention | 0 | 0 | 120
comments | comments | 0 | 0 | 120

    qr <- quality_report(
      responses, instr,
      respondent_id = "respondent_id",
      submitted_at  = "submitted_at",
      started_at    = "started_at"
    )
    quality_summary <- data.frame(
      Metric = c("Respondents", "Items", "Flagged for review", "Flag rate"),
      Value  = c(qr$summary$n_respondents, qr$summary$n_items, qr$summary$n_flagged,
                 sprintf("%.1f%%", 100 * qr$summary$flag_rate)),
      stringsAsFactors = FALSE
    )
    kable(quality_summary, align = c("l", "r"), caption = "Quality screening summary")

Quality screening summary
Metric | Value
--- | ---
Respondents | 120
Items | 15
Flagged for review | 109
Flag rate | 90.8%

## Score scales

`run_analysis_plan()` scores the scales for you, but scoring once up front lets you inspect the construct scores and run the assumption checks below.

    scored    <- score_scales(responses, instr, keep_items = TRUE, keep_meta = TRUE)
    scale_ids <- vapply(instr$scales, function(x) x$id, character(1))
    score_cols <- intersect(scale_ids, names(scored))

    kable(head(scored[, score_cols, drop = FALSE]), digits = 2,
          caption = "Scale scores, first respondents")

Scale scores, first respondents
digital_marketing | service_quality | sustainability | satisfaction | behavioural_intention
--- | --- | --- | --- | ---
2.67 | 3.67 | 5.0 | 3.5 | 4.5
3.00 | 2.67 | 3.0 | 1.5 | 2.5
4.67 | 3.33 | 3.5 | 4.5 | 2.5
4.33 | 4.00 | 5.0 | 4.5 | 5.0
3.00 | 3.67 | 4.0 | 3.0 | 3.5
3.67 | 3.67 | 2.5 | 3.5 | 3.0

The scale-score distributions show the shape of each construct before the plan runs.

    op <- par(mfrow = c(1, length(score_cols)), mar = c(4, 3, 2, 1))
    for (s in score_cols) {
      v <- scored[[s]]; v <- v[is.finite(v)]
      hist(v, col = "#16B3B1", border = "white", main = s,
           xlab = "Score", ylab = "")
    }

    par(op)

## Check assumptions before the plan

`assumption_report()` reports the checks a technique relies on, such as residual normality, variance inflation, and influence for a regression.

    assumption_report(
      scored,
      predictors = c("digital_marketing", "service_quality", "sustainability"),
      outcome    = "satisfaction"
    )
    #> $method
    #> [1] "assumptions"
    #>
    #> $normality
    #> data frame with 0 columns and 0 rows
    #>
    #> $homogeneity
    #> data frame with 0 columns and 0 rows
    #>
    #> $regression
    #> $regression$n
    #> [1] 120
    #>
    #> $regression$residual_shapiro_w
    #> [1] 0.9941063
    #>
    #> $regression$residual_shapiro_p
    #> [1] 0.8980753
    #>
    #> $regression$vif
    #> digital_marketing   service_quality    sustainability
    #>          1.212656          1.200070          1.012163
    #>
    #> $regression$cooks_distance
    #>            1            2            3            4            5            6
    #> 1.184326e-04 9.147336e-03 1.482864e-03 3.583748e-07 2.883541e-03 1.731349e-04
    #>            7            8            9           10           11           12
    #> 1.323697e-02 4.460344e-03 4.542716e-05 4.287879e-02 2.143130e-02 1.509419e-03
    #>           13           14           15           16           17           18
    #> 5.088239e-02 5.722577e-03 2.174172e-07 1.127457e-02 8.743696e-03 2.593670e-03
    #>           19           20           21           22           23           24
    #> 7.553824e-03 2.109049e-02 1.932134e-05 2.370723e-03 3.851322e-04 5.461064e-03
    #>           25           26           27           28           29           30
    #> 1.872377e-04 4.489098e-07 1.706497e-02 2.752537e-06 1.434770e-04 3.195816e-03
    #>           31           32           33           34           35           36
    #> 6.905977e-02 1.536396e-02 1.260801e-02 1.534755e-03 1.380360e-02 6.848461e-03
    #>           37           38           39           40           41           42
    #> 1.700720e-06 3.793174e-04 2.134337e-03 2.287672e-02 1.001287e-02 4.071700e-03
    #>           43           44           45           46           47           48
    #> 5.471299e-03 5.270891e-02 3.433051e-02 2.975180e-03 3.167244e-03 9.174703e-04
    #>           49           50           51           52           53           54
    #> 1.170983e-02 8.648779e-04 1.116591e-03 8.230930e-03 1.381027e-02 3.504105e-02
    #>           55           56           57           58           59           60
    #> 1.571502e-04 2.905749e-03 1.402942e-03 5.808575e-03 2.019476e-03 6.948885e-06
    #>           61           62           63           64           65           66
    #> 1.170618e-03 3.652109e-03 2.922443e-02 9.678057e-03 3.620026e-06 3.218468e-03
    #>           67           68           69           70           71           72
    #> 1.378174e-03 5.716292e-03 3.730149e-03 9.087202e-03 8.357802e-03 1.187750e-02
    #>           73           74           75           76           77           78
    #> 2.430428e-02 3.348263e-04 9.538221e-03 1.257656e-03 1.433252e-03 1.356868e-03
    #>           79           80           81           82           83           84
    #> 2.563597e-05 1.801658e-03 1.835160e-03 5.784606e-04 2.366539e-02 8.884675e-04
    #>           85           86           87           88           89           90
    #> 3.063566e-04 9.322376e-03 1.300065e-02 5.103375e-03 6.710071e-04 3.902158e-03
    #>           91           92           93           94           95           96
    #> 3.851322e-04 3.045158e-03 4.482863e-04 3.504325e-03 3.537797e-03 4.644052e-04
    #>           97           98           99          100          101          102
    #> 1.386934e-02 4.444185e-03 5.864202e-03 9.236185e-03 2.563387e-02 3.445601e-04
    #>          103          104          105          106          107          108
    #> 1.467024e-03 1.170618e-03 9.051071e-04 7.373574e-03 2.295924e-05 6.910949e-04
    #>          109          110          111          112          113          114
    #> 3.504325e-03 2.274586e-04 9.266580e-05 4.857092e-03 4.700977e-08 2.921484e-03
    #>          115          116          117          118          119          120
    #> 4.090240e-03 1.105467e-02 7.365409e-04 1.938023e-02 1.012910e-03 1.535916e-02
    #>
    #> $regression$standardised_residuals
    #>            1            2            3            4            5            6
    #> -0.082923698 -1.870645918  0.395846500  0.004591293 -0.692174489 -0.185004873
    #>            7            8            9           10           11           12
    #>  1.084968504  0.780147050  0.110776991 -2.638742712 -1.795997774  0.319306483
    #>           13           14           15           16           17           18
    #>  1.954971640 -1.219815183  0.004191993  1.118225981  1.039309006  0.527129425
    #>           19           20           21           22           23           24
    #> -0.961835047  0.980099373  0.063225377  0.551354644  0.278271787 -0.647978136
    #>           25           26           27           28           29           30
    #>  0.227581315  0.004986299 -1.172216599  0.021868776 -0.095706217 -0.782623908
    #>           31           32           33           34           35           36
    #> -2.309384417 -1.374470793  0.932880267 -0.690640105  1.255066667  0.671314169
    #>           37           38           39           40           41           42
    #>  0.012570683  0.352662125  0.374079633  1.867781077 -1.228052989  0.606343018
    #>           43           44           45           46           47           48
    #> -0.617848955 -1.662835019  2.074446939  0.978590982 -0.793286443  0.252351509
    #>           49           50           51           52           53           54
    #>  1.156356561 -0.449473176 -0.363323806  1.152172372 -1.440809735 -1.866773520
    #>           55           56           57           58           59           60
    #>  0.189559954 -0.858506945 -0.309992596 -1.198889104  0.696726010 -0.035497078
    #>           61           62           63           64           65           66
    #> -0.594649744  0.646085118  1.892162918  1.346958028  0.012140769 -1.050312804
    #>           67           68           69           70           71           72
    #>  0.598618566 -1.508218399 -0.521321175 -0.874142231  1.497519709  1.032511438
    #>           73           74           75           76           77           78
    #>  2.208163710  0.178475846  1.021361391  0.616360158 -0.448239907 -0.298894184
    #>           79           80           81           82           83           84
    #> -0.041513884  0.484623799 -0.711839662 -0.445277725  2.271897566  0.424028147
    #>           85           86           87           88           89           90
    #>  0.236369610  1.923468863 -1.537113657 -0.910885755  0.287247717  0.885761213
    #>           91           92           93           94           95           96
    #>  0.278271787 -1.099326747 -0.299846917 -0.670336465  1.075297214  0.241274096
    #>           97           98           99          100          101          102
    #>  1.521915286  0.863717913  0.560137775 -0.839407809 -1.485896784 -0.173199125
    #>          103          104          105          106          107          108
    #> -0.230361700 -0.594649744  0.499913855 -1.172602644 -0.046456880 -0.397268813
    #>          109          110          111          112          113          114
    #> -0.670336465  0.181786019 -0.202323795 -0.825310891  0.001891811 -0.577856052
    #>          115          116          117          118          119          120
    #> -0.520419080  1.827370061 -0.348396466 -1.215115771  0.250475472  0.752951062
    #>
    #>
    #> $expected_counts
    #> NULL
    #>
    #> $apa
    #> [1] "Assumption checks were computed."
    #>
    #> $prompt
    #> [1] "Report assumption checks before interpreting inferential models, especially sparse cells, non-normal residuals, and high VIF values."
    #>
    #> attr(,"class")
    #> [1] "sframe_assumption_report"

## Define the plan

Each block binds a research question to a technique and to the variables that fill each role. A correlation expects `x` and `y`. A regression expects `predictors` and a `dependent` variable. A group comparison expects a `group` and an `outcome`.

    instr$analysis_plan <- list(
      list(id = "RQ1",
           research_question = "Is digital marketing perception associated with satisfaction?",
           family = "association", method = "correlation_pearson",
           roles = list(x = "digital_marketing", y = "satisfaction"),
           options = list(alpha = 0.05)),
      list(id = "RQ2",
           research_question = "Do the three perception scales predict satisfaction?",
           family = "regression", method = "regression_linear",
           roles = list(predictors = c("digital_marketing", "service_quality", "sustainability"),
                        dependent = "satisfaction"),
           options = list(alpha = 0.05)),
      list(id = "RQ3",
           research_question = "Do first-time and repeat visitors differ in behavioural intention?",
           family = "group_comparison", method = "mann_whitney",
           roles = list(group = "visit_type", outcome = "behavioural_intention"),
           options = list(alpha = 0.05))
    )

## Run the plan

    results <- run_analysis_plan(responses, instr)
    results_table(results)

RQ | Research question | Method | Result (APA) | Effect
--- | --- | --- | --- | ---
RQ1 | Is digital marketing perception associated with satisfaction? | pearson | r(118) = 0.54 [0.40, 0.65], p < .001 | large
RQ2 | Do the three perception scales predict satisfaction? |  | R² = 0.383, F(3, 116) = 23.95, p < .001 |
RQ3 | Do first-time and repeat visitors differ in behavioural intention? |  | U = 1576, z = -0.98, p = 0.327, r = 0.09 [0.00, 0.27] | negligible

Pass `plots = TRUE` to attach a brand-styled `ggplot2` chart to each result that supports one (descriptive, correlation, chi-square, and regression blocks). The chart sits in `$plot` alongside the existing `$table` and `$apa` elements.

    results_plots <- run_analysis_plan(responses, instr, plots = TRUE)
    results_plots[[1]]$plot

## Read a single result

Each result holds more than the printed line. It carries the APA statistic, the effect-size label where the technique reports one, a writing prompt, and the references that support the technique.

    rq1 <- results[[1]]

    rq1$apa
    #> [1] "r(118) = 0.54 [0.40, 0.65], p < .001"
    rq1$effect_label
    #> [1] "large"
    rq1$prompt
    #> [1] "There was a positive, large significant correlation between digital_marketing and satisfaction, r(118) = 0.54 [0.40, 0.65], p < .001. Explain what this means for your research question."
    unlist(rq1$citations)
    #>                                                                                                                                                         field_2018
    #>                                                                            "Field, A. (2018). *Discovering statistics using IBM SPSS statistics* (5th ed.). SAGE."
    #>                                                                                                                                                         cohen_1988
    #>                                                          "Cohen, J. (1988). *Statistical power analysis for the behavioral sciences* (2nd ed.). Lawrence Erlbaum."
    #>                                                                                                                                                             r_core
    #>                                          "R Core Team. (2026). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing."
    #>                                                                                                                                                        surveyframe
    #> "Sharafuddin, M. A. (2026). *surveyframe: Survey Instrument Workflows* (Version 0.3.4) [Computer software]. https://github.com/MohammedAliSharafuddin/surveyframe"

## Render the results report

`render_results()` writes a self-contained HTML report with one section per research question, each holding the APA result, the writing prompt, a space for the interpretation, and a reference list compiled from the techniques used.

    render_results(results, instr, output_file = "results.html", citation_format = "apa")

## In SurveyStudio

SurveyStudio runs the same plan. Open it on the analysis screen, upload the responses, and the Analysis Plan screen runs the saved plan and shows a table of each question with its method and APA result. The full report is produced on the Export screen.

    launch_studio(
      instrument     = instr,
      responses      = responses,
      screen         = "analysis",
      launch.browser = FALSE
    )